code: def _pad_to_max(
      x: List[int], max_len: int, padding_id: int = 0
  ) -> List[int]:
      return [padding_id] * (max_len - len(x)) + x
  
file_path: BigDL/python/llm/src/bigdl/llm/vllm/model_executor/models/bigdl_mistral.py
imports: import torch
  from torch import nn
  from transformers import AutoTokenizer, PreTrainedTokenizerBase, LlamaConfig
  from typing import Optional, Tuple, List, Type, Dict
  from bigdl.llm.vllm.sequence import SequenceOutputs, SequenceGroupMetadata
  from bigdl.llm.vllm.model_executor.layers.bigdl_sampler import BigDLSampler
  from bigdl.llm.vllm.model_executor.models.bigdl_model import BigDLModelForCausalLM
  from bigdl.llm.vllm.logger import init_logger
  import math
  import time
  from bigdl.llm.vllm.model_executor.input_metadata import InputMetadata
  from transformers.generation.logits_process import LogitsProcessorList, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper
label_desc: Use image node_id 1 for calling a global function with example usage: _pad_to_max(x, max_len, padding_id) and returns: unknown